Unleashing the Power of MLOps and DataOps in Data Science

Organizations trying to move forward with analytics and data science initiatives -- while floating in an ocean of data -- must enhance their overall approach and culture to embrace a foundation on DataOps and MLOps. Leveraging these operational frameworks are necessary to enable the data to generate real business value.

By Yash Mehta, Founder and CEO of Intellectus.

Data is overwhelming, and so is the science of mining, analyzing, and delivering it for real-time consumption. No matter how much data is good for business, it is still vulnerable to putting the privacy of millions of users at unimaginable risk. That is exactly why there is a sudden inclination towards more automated processes.

In the past year, enterprises sticking to conventional analytics have realized that they will not survive any longer without a makeover. For example, enterprises are experimenting with micro-databases, each storing master data for a particular business entity only. There is also an increase in the adoption of self-servicing practices to discover, cleanse, and prepare data. They have understood the importance of embracing the ‘XOps’ mindset and delegate more important roles to MLOps and DataOps practices.


The need for MLOps


Now, MLOps are important because bringing ML models to execution is more difficult than training them or deploying them as APIs. The complication further worsens in the absence of governance tools. Ultimately, the ML models do not adapt to changes due to dynamic data influx leading to a non-performing end output.

To put it simply, MLOps is everything between an ML model and its execution in live production. The process involves a thorough collaboration of data scientists with operational professionals such as ML/DL engineers. As a team, they experiment on different features such as parameters and hyperparameters. The objective is to speed up the model development & deployment, monitoring and approval of machine learning models.


85% of AI projects are prone to errors


As per Gartner, this occurs largely due to inefficient roadmap to production and communication gaps with the DevOps teams. With the automation in model deployment, MLOps addresses the issues with finesse. Since data science is all about dynamism, MLOps create adaptable transport pipelines, thereby accommodating models of all types. Moreover,

  • It eliminates time-consuming data science algorithms with automation. It enables scientists to foresee results more efficiently.
  • It provides a thorough collaboration between the data scientists, DevOps resources, and developers, thereby simplifying model optimization.
  • It ensures a seamless transition of models from data science to DevOps teams aligned with continuous integration.

While we are at it, Neptune.ai is a case study worth mentioning. The MLOps platform provides a ‘store’ for book-keeping all metadata sets. The Metadata store provides a user-friendly platform to perfect MLOps model management. Here, the data scientists can create logs, store & organize datasets, and query all metadata generated during the lifecycle of the MLOps model. Moreover, the dashboard provides ML experiment tracking, model registry, and instant access to the Metadata Database.

There are others such as Neal Analytics that provide end-to-end lifecycle handling of MLOps. With experience across a range of sectors, the ML services provide roadmap planning, maturity assessment, and model management & execution.

DataOps are a set of best practices that instruct an automated and process-oriented roadmap to enhance the quality and reduce the cycle time of analytics.

Therefore, it drives agility and enables consistent delivery of data to the business. Like MLOps, DataOps also pushes for reusability and flexibility to accommodate new use cases. Moreover, governing data by design creates workflows to automatically implement policies in the new pipelines.

Here, the data consumption is easy, secure and prevents organizations from facing unforeseen issues. The objective is to stream clean data for trustworthy and actionable predictive analytics. Not to miss, defining data roles, formulating data policies, and designing data pipelines are all included in this practice.

Interestingly, customized schemas have gained widespread acclaim recently for their ability to accommodate evolving enterprise facets. Consider K2View that provides a unique approach to collect, clean, analyze and stream data. Their data fabric technology captures data from multiple sources and aligns them in a unified template, thereby easing orchestration, metadata management, and data governance.

In the pursuit of faster data delivery, their technology compliances by the principle of continuity in data science:

  • Continuous Data Integration
    Enabling on-demand data integration and cross-domain management of master data sets. This helps in real-time access to new sources that are reflected in the Fabric data model. This model is the central data repository to access data.
  • Continuous Data Delivery
    Handling all modifications to the fabric model on the go. They are least dependent upon downtimes and perform in run-time without causing any disruption to the delivery.
  • Continuous Data Deployment
    On-demand data accessibility to any requesting application across delivery models such as ETL, virtualization, streaming, web services, or messaging.

Gartner suggests that just by using fabric technology, businesses save 30% integration time, 30% deployment, and 70% of resources in maintenance. Therefore, enterprises across various industrial disciplines have adopted the fabric model to strengthen their DataOps as well as MLOps initiatives.




For enterprises thriving to outpace massive amounts of data, it is imperative to embrace a hybrid mindset. Therefore, a data delivery model supported by DataOps and MLOps ensures a stronger foundation for the long shot. Operationalizing data management at an enterprise scale is the only formula to achieve business agility. Therefore, enabling data that yields business value across dynamic use cases and a hybrid landscape should be the priority.


Bio: Yash Mehta is an IoT and Big Data enthusiast who has contributed many articles on IDG, IEEE, Entrepreneur, etc. publications. He co-developed platforms like Getlua that lets users easily merge multiple files together. He also founded a research platform that generates actionable insights from experts.